SAGE Lab
The Sequence Analysis and Genomics lab at UMass Amherst aims to build and apply computational methods to understand genetic variation. We have a particular emphasis on antibiotic resistant bacteria, which pose a major and evolving public health threat.
Using machine learning combined with a deep understanding of molecular biology and bacterial genomics, we build models that predict antibiotic resistance from genome sequences, and provide interpretable outputs about the genetic basis of resistance.
Selected Publications
Latest News
Our new paper summarizing the ESCMID workshop on Artififical inteliigence and machine learning in medical microbiology diagnostics was published in Microbes and Infection. Dr. Green presented two sessions at this workshop.
New Preprint: Mechanistic evidence that motif-gated domain recognition drives contact prediction in protein language models.
New Preprint: Convolutional neural networks quantify antibiotic resistance in Mycobacterium tuberculosis with diagnostic grade accuracy and predict treatment response
New preprint: AMR-GNN: A multi-representation graph neural network framework to enable genomic antimicrobial resistance prediction. With collaborators at Monash University